Brain scans during the first year can predict autism in high-risk babies

Study needs repeating and only applies to some, but it hints at early detection.

By scanning the growing brains of 148 babies with high and low risks of autism, researchers could predict which children would develop the disorder within the first year of life before symptoms began appearing, and diagnoses were made at two, researchers reported Wednesday in Nature.

Those researchers, led by psychologists Heather Cody Hazlett and Joseph Piven at the University of North Carolina at Chapel Hill, got the idea for the study after finding earlier that children with autism tended to have larger brains than kids without the disorder. To follow up, they used magnetic resonance imaging to track and predict brain overgrowth as it happened. In all, the study raises hope that doctors will one day be able to make diagnoses quickly, allowing for earlier and earlier interventions.

The study has limitations, of course: it was small, so researchers will need to repeat it with far more children to confirm the findings. It also only applies to babies with a high-risk of developing autism, which are babies who have siblings already diagnosed with the disorder. For families with one child with autism, there’s about a one-in-five chance that subsequent children will also be affected. In the general population in the US, autism is diagnosed in about one in 68.

Still, “the findings lay the foundation for the field to move toward attempting to implement interventions before the symptoms that define autism consolidate into a diagnosis,” Jed Elison a child development expert at the University of Minnesota and study co-author, said in a statement.

For the study, researchers took MRI scans of 106 high-risk babies at 6, 12, and 24 months. They did the same for 42 low-risk babies. As before, they noted that children who were later diagnosed with autism at 24 months had bigger brains. More specifically, their brains’ cortical surface area—the folded, rippled outside layer of the brain—grew faster in the first 6 to 12 months compared to those without a diagnosis.

The researchers then used the early scans and machine learning to develop an algorithm that could predict the development of autism. In a test, the algorithm correctly predicted 30 of 37 autism diagnoses, an accuracy rate of 81 percent. For a group of 142 that were not diagnosed with autism, the algorithm incorrectly predicted just four of them would be diagnosed.

The researchers are optimistic but cautious. False positive diagnoses could be devastating for a family, and the technique is just in its infancy. But with more brain scans and data from other types of imaging, the researchers hope that the predictions will become stronger and more widely applicable.